To address the aforementioned issues, we propose Contrastive Learning for Local and Global Learning MRI Reconstruction network (CLGNet), which is composed of spatial branch and wavelet branch, as illustrated in Fig. REF . Firstly, according to the Fourier theory, point-wise update in the Fourier domain globally affects all input features in the Spatial domain, which inspires us another way to learn the global information. Hence, to learn the global information, we introduce the Fast Fourier Convolution (FFC) [1]} to our model and propose the Spatial and Fourier Layer (SFL), which adopts vanilla convolution and FFC to learn the local and global information (see Fig. REF ). Furthermore, based on SFL, we further propose a Spatial and Fourier Residual Block (SFRB) and Spatial and Fourier Residual Block in Residual module (SFRIR) as main components of our model. Furthermore, compared with self-attention and transformer, the proposed SFL can learn global information more effectively in less time. Secondly, we introduce Contrastive Learning (CL) to constrain the upper bound and lower bound of the proposed CLGNet, as shown in Fig. REF . In order to constrain the quality of the recovered image, the following two steps are required: 1) constraining the upper bound and 2) constraining the lower bound. For constraining the upper bound, we need to pull the recovered MR image closer to the clear MR image, and for constraining the lower bound, we need to push the recovered MR image farther away from the undersampled MR image. After the above two steps, the solution space will be successfully constrained and the quality of the recovered MR image will be further improved.
